• Shishir Dash, Kirk H Shelley, David G Silverman, and Ki H Chon.
• Department of Electrical Engineering, State University of New York, Stony Brook, NY 11794, USA. sdash@ic.sunysb.edu
• IEEE Trans Biomed Eng. 2010 May 1;57(5):1099-107.

AbstractWe compare the performance of two different time-frequency-based breathing rate (BR) detection algorithms when used on three different physiological signals: the ECG, the photoplethysmogram (PPG), and the piezoelectric pulse transducer (PZO) signal. Studies carried out over the past have shown the existence of amplitude and/or FMs due to respiration in physiological signals, such as those mentioned. In a recent study, we analyzed the PPG signal and detected the FM and amplitude modulation effect that controlled breathing had on it, and inferred the rate of respiration using the time-frequency spectrum (TFS) (via a wavelet (WT) or complex demodulation (CDM) approach). We showed that such TFS BR detection methods were very accurate and consistently outperformed the exclusively time-domain autoregressive modeling (AR) method, especially in the real-time (data length of 1 min) case. We now explore the possibility of using these methods on the ECG and the finger PZO signal, of which only the former has been previously used with some success to derive BR. Testing performed on 15 healthy human subjects for a range of BR and two body positions showed that though the PPG signal gave the most consistently high performance, the ECG and PZO also proved to be reasonably accurate over longer time segments. Furthermore, the CDM approach was on average either better than or comparable to the WT method in terms of both accuracy and repeatability of the detection.

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